Engineers and analysts (e.g., users) analyzing a database may know where to turn for information about previous queries of the database. Query logs may not be centralized and/or easy to understand. Further, documentation by each user may incomplete, inaccessible, and/or difficult to find. Therefore, it may be difficult for users of the database to work together in creating documentation around different attributes of the database. As a result, each user may maintain in dependent notes and documentation that is not shared with others. Over time, users may end up duplicating work in generating semantically accurate documentation of various attributes (e.g., information that determines the properties of a field or tag) of the database.
As a result, each engineer and/or analyst may have to relearn how the database is organized from scratch, with no guidance from knowledge repositories that may have open-ended and collaborative knowledge through previous interaction with the database with similar queries. As a result, the engineer and/or analyst may spend a substantial amount of time in self learning a detailed understanding of the database schema, design, and/or table structure prior to generating a query by manually observing query logs and database structures. Even when the engineer and/or analysts understands the database, they may waste a significant amount of time in experimentation related to generating semantically accurate queries to the database when seeking an answer sought by the organization. This may be expensive and wasteful for the organization.